Students

ACST4005 – Actuarial Data Analytics

2021 – Session 2, Weekday attendance, North Ryde

Session 2 Learning and Teaching Update

The decision has been made to conduct study online for the remainder of Session 2 for all units WITHOUT mandatory on-campus learning activities. Exams for Session 2 will also be online where possible to do so.

This is due to the extension of the lockdown orders and to provide certainty around arrangements for the remainder of Session 2. We hope to return to campus beyond Session 2 as soon as it is safe and appropriate to do so.

Some classes/teaching activities cannot be moved online and must be taught on campus. You should already know if you are in one of these classes/teaching activities and your unit convenor will provide you with more information via iLearn. If you want to confirm, see the list of units with mandatory on-campus classes/teaching activities.

Visit the MQ COVID-19 information page for more detail.

General Information

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Unit convenor and teaching staff Unit convenor and teaching staff Unit Convenor
Maggie Lee
maggie.lee@mq.edu.au
Lecturer
Pavel Shevchenko
pavel.shevchenko@mq.edu.au
Credit points Credit points
10
Prerequisites Prerequisites
ACST357 or ACST3057
Corequisites Corequisites
Co-badged status Co-badged status
Unit description Unit description

This unit covers tools and techniques in data analytics. Students will be taught how to apply these skills in a range of business environments and will be able to contribute to all stages of developing solutions to analytical problems across multiple industries or domains. This unit has a focus on practical application using a variety of real-life case studies. Students gaining a grade of credit or higher in this unit are eligible for exemption from the Data Analytics Principles subject of the Actuaries Institute.

Important Academic Dates

Information about important academic dates including deadlines for withdrawing from units are available at https://www.mq.edu.au/study/calendar-of-dates

Learning Outcomes

On successful completion of this unit, you will be able to:

  • ULO1: Explain the key iterative steps involved in building a model (business understanding, data understanding and preparation, modelling, evaluation, communication and deployment).
  • ULO2: Describe the various stages in data understanding and preparation and apply these skills within the context of practical problems.
  • ULO3: Compare predictive modelling techniques to select an appropriate method for a stated situation and perform predictive modelling for a given set of data.
  • ULO4: Use a range of perspectives (statistical techniques and measures, business context and objectives etc.) to evaluate the appropriateness of a model.
  • ULO5: Communicate modelling results to a range of business decision making audiences, taking into account the audience’s needs and relating findings back to the original business objectives.

General Assessment Information

Assessment criteria for all assessment tasks will be provided on the unit iLearn site.

It is the responsibility of students to view their marks for each within-session-assessment on iLearn within 20 days of posting. If there are any discrepancies, students must contact the unit convenor immediately. Failure to do so will mean that queries received after the release of final results regarding assessment tasks (not including the final exam mark) will not be addressed.

Late submissions of assessments

Sometimes unavoidable circumstances occur that might prevent you from submitting an assessment on time and, in that case, you may be eligible to lodge a Special Consideration request.

Unless a Special Consideration request has been submitted and approved, please note that no extensions to assessment deadlines will be granted. Assessments that are submitted late will attract a late penalty:

  1. There will be a deduction of 10% of the total available marks for each 24 hour period or part thereof that the submission is late.
  2. No assessment will be accepted more than 72 hours after the original due date and time (incl. weekends).
  3. No late submissions will be accepted for timed assessments (e.g., quizzes, online tests) or for tasks with a weighting of 10% or less.

Assessment Tasks

Name Weighting Hurdle Due
Project 20% No Week 7 - See iLearn for details
Case Studies 20% No Week 12 - See iLearn for details
Final Exam 60% No University Examination Period

Project

Assessment Type 1: Quantitative analysis task
Indicative Time on Task 2: 20 hours
Due: Week 7 - See iLearn for details
Weighting: 20%

Students will be required to write up a report (word limit of up to 5000 words) based on a project.
On successful completion you will be able to:
  • Explain the key iterative steps involved in building a model (business understanding, data understanding and preparation, modelling, evaluation, communication and deployment).
  • Describe the various stages in data understanding and preparation and apply these skills within the context of practical problems.
  • Compare predictive modelling techniques to select an appropriate method for a stated situation and perform predictive modelling for a given set of data.
  • Use a range of perspectives (statistical techniques and measures, business context and objectives etc.) to evaluate the appropriateness of a model.
  • Communicate modelling results to a range of business decision making audiences, taking into account the audience’s needs and relating findings back to the original business objectives.

Case Studies

Assessment Type 1: Case study/analysis
Indicative Time on Task 2: 20 hours
Due: Week 12 - See iLearn for details
Weighting: 20%

Students will work on two individual case studies.
On successful completion you will be able to:
  • Explain the key iterative steps involved in building a model (business understanding, data understanding and preparation, modelling, evaluation, communication and deployment).
  • Describe the various stages in data understanding and preparation and apply these skills within the context of practical problems.
  • Compare predictive modelling techniques to select an appropriate method for a stated situation and perform predictive modelling for a given set of data.
  • Use a range of perspectives (statistical techniques and measures, business context and objectives etc.) to evaluate the appropriateness of a model.
  • Communicate modelling results to a range of business decision making audiences, taking into account the audience’s needs and relating findings back to the original business objectives.

Final Exam

Assessment Type 1: Examination
Indicative Time on Task 2: 28 hours
Due: University Examination Period
Weighting: 60%

The final examination will be closed book, a three-hour written paper with ten minutes reading time, to be held during the University Examination period.
On successful completion you will be able to:
  • Explain the key iterative steps involved in building a model (business understanding, data understanding and preparation, modelling, evaluation, communication and deployment).
  • Describe the various stages in data understanding and preparation and apply these skills within the context of practical problems.
  • Compare predictive modelling techniques to select an appropriate method for a stated situation and perform predictive modelling for a given set of data.
  • Use a range of perspectives (statistical techniques and measures, business context and objectives etc.) to evaluate the appropriateness of a model.
  • Communicate modelling results to a range of business decision making audiences, taking into account the audience’s needs and relating findings back to the original business objectives.

1 If you need help with your assignment, please contact:

  • the academic teaching staff in your unit for guidance in understanding or completing this type of assessment
  • the Writing Centre for academic skills support.

2 Indicative time-on-task is an estimate of the time required for completion of the assessment task and is subject to individual variation

Delivery and Resources

Classes

ACST4005 is offered via classes on North Ryde campus (Macquarie University). Students share lecture classes and a common teaching website with the units ACST8095 and ACST7095. 

Downloadable lecture recordings

In all weeks, standard recordings of campus lectures using the University's lecture recording facility (ECHO360 or zoom) will be available. The recordings capture audio and screenshot. The recordings will either be provided via the ECHO360 link which is located on the right hand side of the webpage or via a zoom link.  

Timetable

The timetable for classes can be found on the Macquarie University website at: http://www.timetables.mq.edu.au

Alterations to the class times or locations will be advised in class and on the teaching website.

Teaching staff

Maggie Lee is the unit convenor and will be taking five weeks of classes. Maggie can be contacted via Dialogue on the website, or during her consultation hours.

Professor Pavel Shevchenko will be taking the other weeks of classes. Pavel can be contacted via Dialogue on the website, or during his consultation hours.

Hong Xie is the teaching administrator, and can deal with any administrative queries related to the unit. Hong can be contacted via Dialogue on the website.

Assumed knowledge

We assume from the start of the Actuarial Data Analytics that you have acquired the knowledge and skills in subjects from the Foundation Program (Part 1s) of the Actuaries Institute education program.

Lecture slides/Learning Guide

There will be Lecture Slides and/or Learning Guides and associated readings for each section of work. You should read these materials in advance of the lectures, and bring a copy with you to classes.

Technology Used and Required

In this unit, you will need to have access to and to be able to use software to code (R and R studio) and word-processing software to produce reports.

Teaching Website

Course material is available on the online learning management system (iLearn). The teaching website is integral to this unit. Passive involvement in this unit greatly reduces the likelihood of achieving the exemption standard of understanding. Interaction with other students and with teachers is very important, and the website is the forum for that interaction. You will need to be accessing the website regularly to see announcements, read postings and stay informed - at least every couple of days. This is your responsibility and we cannot make any allowances for students who miss important information due to not checking the website regularly. The website entry page is at: http://ilearn.mq.edu.au

Teaching and Learning Activities

The unit is taught as set out in the Classes section. The Unit Schedule sets out the assessment and the topics covered in each week of the session.

Exemptions

The Macquarie University unit ACST4005/ACST7095/ACST8095 will satisfy the requirements for exemption from the Data Analytics Principles subject of the Actuary program of the Actuaries Institute. You will be recommended for exemption if you attain grades of Credit or better in this unit. It is the responsibility of the student to apply to Macquarie University to recommend them to the Actuaries Institute for professional exemptions. For information about this process please contact Hong Xie via iLearn.

Unit Schedule

Week

Week beginning

Topic

Lecturer

 Assessment task

1

26-Jul

 Business Environment ML  

2

02-Aug

 Communication

ML

  

3

09-Aug

 Data exploration ML  

4

16-Aug

Data quality

ML

  
5 23-Aug Data manipulation and cleansing ML  

6 

 30-Aug

Basic Concepts and Linear Regression

PS

  

7 

 06-Sep Linear Regression II PS Project
Break 13-Sep      

Break

20-Sep

 

 

  

8

 27-Sep

Model Selection

PS

  

9

04-Oct

GLM (Poisson Regression), clustering

 PS  

10

11-Oct

Regression Tree methods

PS

  

11

18-Oct

Classification

PS

  

12 

 25-Oct

Neural Networks and Generalised Additive Models

PS

 Case Studies

13 

 01-Nov

Mortality modelling using regression tree

PS

  

Policies and Procedures

Macquarie University policies and procedures are accessible from Policy Central (https://policies.mq.edu.au). Students should be aware of the following policies in particular with regard to Learning and Teaching:

Students seeking more policy resources can visit Student Policies (https://students.mq.edu.au/support/study/policies). It is your one-stop-shop for the key policies you need to know about throughout your undergraduate student journey.

To find other policies relating to Teaching and Learning, visit Policy Central (https://policies.mq.edu.au) and use the search tool.

Student Code of Conduct

Macquarie University students have a responsibility to be familiar with the Student Code of Conduct: https://students.mq.edu.au/admin/other-resources/student-conduct

Results

Results published on platform other than eStudent, (eg. iLearn, Coursera etc.) or released directly by your Unit Convenor, are not confirmed as they are subject to final approval by the University. Once approved, final results will be sent to your student email address and will be made available in eStudent. For more information visit ask.mq.edu.au or if you are a Global MBA student contact globalmba.support@mq.edu.au

Student Support

Macquarie University provides a range of support services for students. For details, visit http://students.mq.edu.au/support/

Learning Skills

Learning Skills (mq.edu.au/learningskills) provides academic writing resources and study strategies to help you improve your marks and take control of your study.

The Library provides online and face to face support to help you find and use relevant information resources. 

Student Services and Support

Students with a disability are encouraged to contact the Disability Service who can provide appropriate help with any issues that arise during their studies.

Student Enquiries

For all student enquiries, visit Student Connect at ask.mq.edu.au

If you are a Global MBA student contact globalmba.support@mq.edu.au

IT Help

For help with University computer systems and technology, visit http://www.mq.edu.au/about_us/offices_and_units/information_technology/help/. 

When using the University's IT, you must adhere to the Acceptable Use of IT Resources Policy. The policy applies to all who connect to the MQ network including students.

Unit information based on version 2021.01R of the Handbook